An AI agent evaluating a purchase on a consumer’s behalf doesn’t browse your website and form a considered view the way a person would. It queries structured knowledge, retrieves context where it exists, weighs a brand against a set of criteria, and produces a recommendation — often in a single turn.
Until now, no standard existed for a brand to declare its decision-stage evidence in a format built for that process rather than for a human reader or a search crawler. That’s the gap the Linkage Gap research exposed: across 1,427 probes, 87.3% of brands present when a conversation began were displaced before the recommendation — usually because the model had the relevant facts, but didn’t carry them to the moment the decision was made. brand.context is the standard built to close that.
One file, published where agents look
brand.context is an open, machine-readable JSON-LD file a brand publishes at a predictable path on its own domain:
https://yourbrand.com/.well-known/brand.contextIt declares the specific, structured evidence that determines whether a brand survives to an AI’s final recommendation — the right facts, in the right form, ready to be surfaced at the decision turn. It’s a formal specification: it uses RFC 2119 conformance language (MUST / SHOULD / MAY), commits to a six-month deprecation window on breaking changes, and grounds every claim in published, DOI-cited research.
You cite — you don’t just claim
What separates brand.context from a marketing file is that it’s built to be believed by a machine that has learned to discount hype. Two fields do that work.
Verified vs self-declared
Every claim is tagged by how checkable it is. A consuming AI should weight the two very differently.
What evidence can, and can’t, fix
Every field is honest about what publishing it will achieve — a rare thing for a brand standard to admit.
Evidence, by category
Beyond a brand-identity layer (so the model links to the right entity) and a competitive-positioning layer, the core of the schema is eight evidence categories — a brand fills in the ones that apply to its sector.
Clinical evidence
Active ingredients and cited clinical backing — skincare, supplements, OTC health.
Lifestyle fit
Which purchase criteria you lead on — banking, automotive, subscriptions.
Regulatory access
Which formulation is available, and where — OTC and nutraceuticals.
Technology currency
Your core technology vs a rival’s “newer-science” framing — electronics, SaaS, haircare.
Product version
Your current model vs your own superseded versions still in training data.
Contextual relevance
Where you genuinely win vs your general-category standing (a reasoning-risk field).
Availability
Where you actually ship, sell and fulfil — regional and expanding brands.
Historical narrative
Current, verifiable status vs an outdated decline event in the public record.
Not another sitemap
A whole machine-readable layer is forming for AI — schema.org, LLMs.txt, MCP, Google’s Open Knowledge Format and Agentic Resource Discovery, EntityMap. Those address discovery and packaging: which pages matter, what a domain can do, how knowledge is bundled for retrieval. None of them take a position on which facts about a brand determine a purchase outcome.
It’s complementary, not competitive — a brand can publish an entity index for general knowledge and a brand.context file for the evidence that decides the sale.
Honest about where it stands
The standard is candid about its own maturity, because a standard that overclaims can’t be trusted. There is no guaranteed consumption mechanism today — no major AI platform yet crawls for brand.context files by name. (This isn’t unique to it: independent data found 97% of published LLMs.txt files received zero requests.)
So why publish now? The present value is real but partial: retrieval-augmented systems that index structured web content can already pick it up, and it’s a forward position for the context-graph infrastructure now being built. It’s an early investment with an immediate-but-partial return — and the standard says so, in every field where the distinction applies.
Where this sits
Read the complete standard
This article is an overview. The full v2.0 specification defines the conformance rules, the complete field-by-field schema, the evidence-confidence model, the relationship to other standards and the honest limitations — published open-access on Zenodo with a permanent DOI.
Citation: de Rosen, T. & Sheals, P. (2026). brand.context v2.0: A Machine-Readable Evidence Standard for Closing the Linkage Gap in Agentic Commerce. AIVO Standard. Zenodo. https://doi.org/10.5281/zenodo.21262005 · Licensed CC-BY-4.0.